JP2006050834A - Risk management support system for power supply enterprise - Google Patents

Risk management support system for power supply enterprise Download PDF

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JP2006050834A
JP2006050834A JP2004230057A JP2004230057A JP2006050834A JP 2006050834 A JP2006050834 A JP 2006050834A JP 2004230057 A JP2004230057 A JP 2004230057A JP 2004230057 A JP2004230057 A JP 2004230057A JP 2006050834 A JP2006050834 A JP 2006050834A
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power
amount
demand
prediction
calculating
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JP4154373B2 (en
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Yuji Manabe
裕司 真鍋
Tomomasa Nakada
智将 仲田
Shigeru Kawamoto
茂 川本
Yasuhiro Kobayashi
康弘 小林
Masahiro Arakawa
正浩 荒川
Tadao Nemoto
忠男 根本
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Hitachi Ltd
株式会社日立製作所
Hitachi Eng Co Ltd
日立エンジニアリング株式会社
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

<P>PROBLEM TO BE SOLVED: To rationally predict electric energy to be reserved by procuring from an outside source at prediction point in time by which a power procurement cost becomes minimum or a revenue becomes maximum. <P>SOLUTION: A risk management support system for a power supply enterprise is applied to the power supply plan of a power enterprise. In the system, electricity is generated at in-house facilities or facilities of a contracted company, electric power is procured by purchasing the power procured from the outside source such as an outside industry or the like, and it is supplied to a customer. The system requires at least a data input means, a demand predicting means, a predicting means of probable generated energy, and a calculating means of the generated energy to be procured from the outside source. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

本発明は、特定規模電力事業者(PPS)の事業収益リスクを推定するシステムに関する。   The present invention relates to a system for estimating business profit risk of a specific scale electric power operator (PPS).
昨今の電力小売自由化によって、従来の電力会社でなくても電力を小売できるようになり、新規に特定規模電力事業者(PPS)が出現した。小売自由化の対象は、2004年4月から500キロワット以上の需要家、2005年4月からは50キロワット以上の需要家にまで広がり、工場や商業施設などの需要家の多くが、価格やサービスを見比べて好きな会社から電力を買えるようになる。   With the recent liberalization of power retailing, it has become possible to retail electricity without being a traditional power company, and a new specific-scale power operator (PPS) has emerged. The target of retail liberalization has spread to customers over 500 kilowatts since April 2004, and more than 50 kilowatts since April 2005, and many customers such as factories and commercial facilities offer prices and services. You can buy electricity from your favorite company.
自由化前の電力会社は、各管轄地域の需要家全てに電力を供給したのに対し、自由化によって出現した特定規模電力事業者は、供給できる総電力量が限られており、特定の1件以上の需要家のみと電力取引契約をする。そして、収益の期待値とリスクの観点から、現状の事業性を評価したり、電力取引契約をする需要家を決定したりする。そこで、特定の1件以上の需要家を顧客とした場合の収益の期待値と分散を、より正確に推定することが求められる。   While electricity companies before liberalization supplied power to all customers in each jurisdiction, specific-scale power utilities that emerged as a result of liberalization had a limited total amount of electricity that could be supplied. Sign power contracts with only more customers. Then, from the viewpoint of the expected value of profit and the risk, the current business feasibility is evaluated, and the customer who makes the power transaction contract is determined. Therefore, it is required to more accurately estimate the expected value and variance of revenue when one or more specific customers are customers.
そもそも特定規模電力事業者の収益は、需要家に電力を売って得る収入から、電力調達コストや、電力の託送費,販売費などを差し引いたものである。ここで、電力調達コストについて詳しく説明する。PPSは一般に、電力取引契約している発電所もしくは自社の発電所から主に電力を調達し、他にも電力会社や卸電力市場や他の特定規模電力事業者から調達する。本文中では便利のため、契約している発電所もしくは自社の発電所以外の電力を、「外部調達電力」と呼ぶことにする。但し、これは一般的な呼称ではない。外部調達電力のうち、電力会社から購入する電力をバックアップ電力(以下BU電力)と呼ぶが、BU電力は、例えば前日の朝10時までなど、前もって購入量を予約しなければならないことになっている。当日になって、「発電所からの電力量+BU電力量+その他の外部調達電力量」が需要家の「需要量」に満たず、供給不足となる場合でも、予約量以上の電力を電力会社から買うことはできるが、供給不足となった量や時間に応じて、通常より高い費用を支払わなければならない。また、卸電力市場でも、翌日以降の電力を予約購入することができる。そのため、電力調達コストは、契約発電所の電力と外部調達電力をいくらずつ調達したかと、前日までにどれだけ外部調達電力を予約したかによって決まる。   In the first place, the profit of a specific scale electric power company is obtained by subtracting the power procurement cost, the power consignment cost, the sales cost, etc. from the income obtained by selling power to the consumer. Here, the power procurement cost will be described in detail. In general, PPS procures power mainly from power plants that have a power trading contract or from its own power plant, and from other power companies, wholesale power markets, and other specific power companies. In the text, for convenience, power other than contracted power plants or in-house power plants is called “externally procured power”. However, this is not a general name. Of the externally procured power, the power purchased from the power company is called backup power (hereinafter referred to as BU power), but BU power must be reserved in advance, for example, until 10 am the previous day. Yes. Even if the amount of power from the power plant + BU power amount + other externally procured power amount is less than the customer's "demand amount" and the supply is insufficient, the power company will supply more power than the reserved amount. Can be purchased from, but depending on the amount and time of supply shortage, you have to pay higher than usual. In addition, in the wholesale power market, it is possible to make a reservation purchase of power from the next day. For this reason, the power procurement cost depends on how much of the contracted power plant and externally procured power has been procured and how much of the externally procured power has been reserved by the previous day.
ここで、実際の日々の業務において、特定規模電力事業者が契約発電所以外の電力の予約量を決定する方法について説明する。まず、需要家の翌日の需要量と、自社の発電所もしくは契約している発電所の翌日の発電可能量を予測する。但し、ここでの予測とは、なんらかの方法で予測値を得ることであり、例えば契約している発電所の発電可能量の予測値を、発電所から通知してもらうなどしてもよい。そして、需要量と発電可能量の予測値をもとに、外部調達電力の予約量を決定する。   Here, a description will be given of a method in which a specific-scale power company determines a reserved amount of power other than the contract power plant in actual daily work. First, the demand amount on the next day of the customer and the power generation possible amount on the next day of the company's own power plant or contracted power plant are predicted. However, the prediction here is to obtain a predicted value by some method. For example, the predicted value of the power generation capacity of the contracted power plant may be notified from the power plant. Then, the reserved amount of the externally procured power is determined based on the predicted value of the demand amount and the power generation possible amount.
予約量を決定する際、予想される不足量、即ち需要予測値−発電可能量予測値だけ予約するのではなく、需要量に対して供給量が足りなくなって、電力会社に多額の費用を支払うという事態を避けるために、少し多めに予約することになる。しかし、どれだけ多めに予約するかによって、電力調達コスト(発電所からの電力調達コスト+外部調達電力調達コスト)が大きく異なる。さらに、需要予測が当たりにくい需要家ほど、安全のため、外部調達電力量を多めに予約しなければならない。外部調達電力量を多めに予約しない場合は、供給不足が起こる確率が高くなり、電力会社に対して多額の費用を支払わなければならない。よって、需要量が同じような需要家でも、需要予測の当たりやすさによって、電力を調達するのにかかる総費用が異なる。   When deciding on the reservation amount, instead of reserving only the expected shortage amount, that is, the predicted value of demand-the predicted amount of power generation, the supply amount is insufficient with respect to the demand amount and pays a large amount of money to the power company To avoid this situation, we will make a little more reservations. However, depending on how many reservations are made, the power procurement cost (power procurement cost from the power plant + external procurement power procurement cost) varies greatly. Furthermore, customers who have difficulty in predicting demand must reserve a larger amount of externally procured power for safety. If you do not reserve a large amount of externally procured power, there is a high probability that supply shortages will occur, and you will have to pay a large amount of money to the power company. Therefore, even if the demand is the same, the total cost for procuring electric power varies depending on the ease of demand prediction.
ところで、各需要家の電力使用実績データと発電所特性データと発電所契約条件データと電力会社契約条件データを用いて、電力小売の運用収益とリスクを計算するシミュレーションシステムが、特許文献1に開示されている。   By the way, Patent Document 1 discloses a simulation system that calculates operating revenue and risk of electric power retail using power usage record data, power plant characteristic data, power plant contract condition data, and power company contract condition data of each consumer. Has been.
特開2003−70164号公報JP 2003-70164 A
しかし、特許文献1には、使用するデータの説明は記されているものの、具体的なシミュレーション方法が記されていない。また、需要予測手段や外部調達電力量算出手段が備わっていないため、電力調達コストを正確に推定することができず、電力小売の運用収益を高い精度で推定することができない。   However, Patent Document 1 describes the data to be used, but does not describe a specific simulation method. In addition, since the demand prediction means and the externally procured power amount calculation means are not provided, the power procurement cost cannot be estimated accurately, and the operation profit of the power retail cannot be estimated with high accuracy.
この点について、具体的に簡単な例を挙げて説明する。ある日のある時刻における、発電所の発電可能量=1100kW,需要量=1400kW,前日に予測した発電可能量予測値=1000kW,前日に予測した需要予測値=1500kW,前日に予約した外部予約量=600kWとし、外部調達電力の調達コスト=15円/kW、発電所の電力調達コスト=10円/kWとする。外部予約量分は必ず買わなければならないので、このときの電力調達コストは、1400kWの需要に対し、600kWの外部調達電力と800kWの発電所の電力でまかなう。よって、電力調達コスト=600×15+800×10=
17,000円 である。しかし、外部調達電力量算出手段を備えていないシミュレーションシステムでは、1400kWの需要に対し、外部予約量がいかほどかがわからない。そこで、例えば電力調達コストが最小となるような調達方法、即ち300kWの外部調達電力と1100kWの発電所からの電力によって1400kWの需要をまかなったとする。すると、電力調達コスト=300×15+1100×10=15,500円 となり、
1,500円 の誤差がある。これでは、PPSの収益を高い精度で推定しているとは言えない。尚、例として用いた数字は、現実のものとは異なるので注意されたい。
This point will be described with a specific simple example. At a certain time on a certain day, the possible power generation amount of the power plant = 1100 kW, the demand amount = 1400 kW, the predicted power generation amount predicted on the previous day = 1000 kW, the predicted demand value predicted on the previous day = 1500 kW, the external reservation amount reserved on the previous day = 600 kW, procurement cost of externally procured power = 15 yen / kW, and power procurement cost of power plant = 10 yen / kW. Since the amount of the external reserved amount must be purchased, the power procurement cost at this time is covered by the externally procured power of 600 kW and the power of the 800 kW power plant for the demand of 1400 kW. Therefore, power procurement cost = 600 × 15 + 800 × 10 =
It is 17,000 yen. However, in a simulation system that does not include an externally procured power amount calculation means, it is difficult to know how much the external reservation amount is for a demand of 1400 kW. Thus, for example, suppose that a procurement method that minimizes the power procurement cost, that is, an externally procured power of 300 kW and an electric power from a 1100 kW power plant met the demand of 1400 kW. Then, power procurement cost = 300 x 15 + 1100 x 10 = 15,500 yen,
There is an error of 1,500 yen. In this case, it cannot be said that PPS revenue is estimated with high accuracy. Note that the numbers used as examples are different from the actual ones.
本発明が解決する課題は、電力調達コストが最小、もしくは収益が最大となるような外部調達電力予約量を、予約する時点で合理的に予測することである。   The problem to be solved by the present invention is to reasonably predict an externally procured power reservation amount at which the power procurement cost is minimized or the profit is maximized at the time of reservation.
自社設備もしくは契約先設備で発電するとともに、外部事業者等の外部調達電力を購入することにより電力を調達して、需要家に供給する電力事業の電力供給計画に適用される電力供給事業リスク管理支援システムには以下のような手段が必要である。
1.データ入力手段
2.需要予測手段
3.発電可能量予測手段
4.外部調達電力量算出手段
Electricity supply business risk management applied to the power supply plan of the electric power business that generates power from in-house facilities or contracted facilities and purchases externally procured power from outside companies, etc., and supplies it to consumers The support system needs the following means.
1. Data input means Demand forecasting means 3. Predictable power generation means 4. Externally procured power calculation method
本発明により、電力調達コストが最小、もしくは収益が最大となるような外部調達電力予約量を、予約する時点で合理的に予測することができる。   According to the present invention, an externally procured power reservation amount that minimizes the power procurement cost or maximizes the profit can be reasonably predicted at the time of reservation.
以下、図面を用いて本発明を実施するための最良の形態の一例について説明する。図1に、本発明の機能ブロック図を示す。データ入力手段(101),需要予測手段(110),発電可能量予測手段(111),外部調達電力量算出手段(106),電力調達コスト算出手段(107),事業収益算出手段(108),事業収益表示手段(109)から成る。さらに、需要予測手段(110)は需要量期待値予測手段(102)と需要予測誤差分布算出手段(103)から成り、発電可能量予測手段(111)は発電可能量期待値予測手段(104)と発電可能量予測誤差分布算出手段(105)から成る。   Hereinafter, an example of the best mode for carrying out the present invention will be described with reference to the drawings. FIG. 1 shows a functional block diagram of the present invention. Data input means (101), demand prediction means (110), power generation possible quantity prediction means (111), externally procured power amount calculation means (106), power procurement cost calculation means (107), business revenue calculation means (108), It comprises business revenue display means (109). Further, the demand forecasting means (110) comprises a demand quantity expected value forecasting means (102) and a demand forecast error distribution calculating means (103), and the power generation possible quantity prediction means (111) is a power generation possible quantity expected value prediction means (104). And a power generation possible amount prediction error distribution calculating means (105).
本発明はこれらの手段により、PPSが一定期間事業を行ったときの期待収益と分散を推定し、その結果を表示する。例えば、1年間の期待収益と分散を推定する場合、複数年分の実データをもとに各年の収益を求め、その期待値と分散を算出し、これを推定値として表示する。もしくは、複数年分の実データの代わりに、例えば実データをもとに生成した1000組の乱数をもとに各年の収益を求め、その期待値と分散を算出してもよい。   The present invention estimates the expected return and variance when the PPS has been operating for a certain period of time by these means, and displays the result. For example, when estimating expected profit and variance for one year, the profit for each year is calculated based on actual data for a plurality of years, the expected value and variance are calculated, and this is displayed as an estimated value. Alternatively, instead of the actual data for a plurality of years, for example, the profit for each year may be obtained based on 1000 sets of random numbers generated based on the actual data, and the expected value and variance may be calculated.
ここから、1000組の乱数をもとに期待収益と分散を推定・表示する場合を例に挙げて、各手段について詳しく説明する。   From here, each means will be described in detail, taking as an example a case where expected return and variance are estimated and displayed based on 1000 sets of random numbers.
データ入力手段(101)は、需要予測手段(110),発電可能量予測手段(111),外部調達電力量算出手段(106),電力調達コスト算出手段(107),事業収益算出手段(108)で必要な情報をシステムのデータベース(以下、DBとする。)に入力する。需要予測手段(110)では、図2に示すような、カレンダー(曜日,祭日など),需要家のイベント(定休日,セール,開店・閉店時間の変更,棚卸など),天気(晴,曇,雨など),過去の需要量,最高気温や最低気温、などの乱数が、例えば1年分×1000組必要である。発電可能量予測手段(111)では、発電機の特性に関する情報(気温と発電機の最大出力の関係など)と、気温などの乱数、例えば1年分×1000組が必要である。また、発電所が発電した電力の一部を社内で使用する場合は、その需要を予測するため、需要予測手段(110)と同様のデータも必要である。外部調達電力量算出手段
(106)と電力調達コスト算出手段(107)では、外部調達電力の価格の情報、例えば電力会社から調達する場合は、電力会社との約款の情報が必要である。尚、電力会社の場合、実際の調達量と前日時点での予約量との差の大きさ等によって価格が違うなど、価格体系はやや複雑である。事業収益算出手段(108)では、各需要家への電力卸売価格や、電力の託送費の情報が必要である。
The data input means (101) includes a demand prediction means (110), a power generation possible quantity prediction means (111), an externally procured power amount calculation means (106), a power procurement cost calculation means (107), and a business revenue calculation means (108). The necessary information is input to the system database (hereinafter referred to as DB). In the demand prediction means (110), as shown in FIG. 2, a calendar (day of the week, holiday, etc.), a customer event (regular holiday, sale, opening / closing time change, inventory, etc.), weather (clear, cloudy, For example, a random number such as rain), past demand, maximum temperature and minimum temperature, for example, 1000 sets for one year are required. The power generation amount predicting means (111) requires information on the characteristics of the generator (such as the relationship between the temperature and the maximum output of the generator) and random numbers such as the temperature, for example, 1 year × 1000 pairs. In addition, when a part of the power generated by the power plant is used in-house, the same data as the demand prediction means (110) is also required to predict the demand. In the externally procured power amount calculating means (106) and the electric power procurement cost calculating means (107), information on the price of the externally procured power, for example, information on terms and conditions with the electric power company is necessary when procuring from the electric power company. In the case of an electric power company, the price system is somewhat complicated, for example, the price varies depending on the difference between the actual procurement amount and the reservation amount as of the previous day. The business profit calculation means (108) needs information on the wholesale power price for each customer and the consignment cost of power.
次に、需要量期待値予測手段(102)について説明する。(102)の処理フローを、図3に示す。需要予測モデル群DB(301)には、様々な需要予測モデルを格納する。予測モデルの例として、まず回帰モデルを挙げる。その説明変数としては、最高気温や最低気温や過去の需要量などの量的変数や、需要家のイベント(定休日,セール,開店・閉店時間の変更,棚卸など)やカレンダー(曜日,祭日など)や天気(晴,くもり,雨など)などの質的変数が考えられる。つまり、電力需要量=α×前日の最高気温+βや、電力需要量=α×最高気温の予報値+β×最高気温の予報値の2乗+γや、電力需要量=α×前日の同時刻の需要量+β×前々日の同時刻の需要量+γなど、様々な回帰モデルが考えられる。αやβなどのパラメータは、最小二乗法により求めるとよい。これら説明変数の組み合わせが異なる回帰モデルは、別のモデルとして区別してDB(301)に格納する。尚、回帰モデルの入門書としては、サイエンス社の「多変量解析法入門(永田靖・棟近雅彦共著)」の第4章〜6章などを参照されたい。   Next, the demand amount expected value predicting means (102) will be described. The processing flow of (102) is shown in FIG. Various demand prediction models are stored in the demand prediction model group DB (301). As an example of the prediction model, a regression model is given first. The explanatory variables include quantitative variables such as maximum and minimum temperatures and past demand, customer events (regular holidays, sales, changes in opening and closing hours, inventory, etc.), and calendars (day of week, holidays, etc.) ) And weather (clear, cloudy, rain, etc.). That is, electric power demand = α × highest temperature of the previous day + β, electric power demand = α × highest temperature forecast value + β × highest temperature forecast squared + γ, and electric power demand = α × the same time of the previous day Various regression models are conceivable, such as demand amount + β × demand amount at the same time the previous day + γ. Parameters such as α and β may be obtained by the least square method. These regression models with different combinations of explanatory variables are distinguished as different models and stored in the DB (301). For an introduction to regression models, refer to Chapters 4 to 6 of “Introduction to Multivariate Analysis (Joint Nagata and Masahiko Munechika)” of Science.
回帰モデルの他に、過去の一定期間の実績値の平均値を予測値とする方法、気象やカレンダーなどの情報をもとに検索した類似日の実績値を予測値とする方法、ニューラルネットによる方法なども予測モデルの例として挙げられる。尚、ニューラルネットワークの参考文献としては、朝倉書店の「非線形多変量解析(豊田秀樹著)」などがある。   In addition to the regression model, a method that uses the average value of actual values over a certain period in the past as a predicted value, a method that uses actual values for similar days searched based on information such as weather and calendar, etc., and a neural network Methods are also examples of prediction models. As a reference for neural networks, there is “Nonlinear Multivariate Analysis (by Hideki Toyoda)” by Asakura Shoten.
過去の需要量,気象情報,カレンダー,イベントDB(302)には、図4に示すような、各需要家の過去の単位時間(1時間や30分)ごとの需要量,需要家の所在地の気象情報(気温,天候など),カレンダー(曜日,祭日など),需要家ごとのイベント(定休日,セール,開店・閉店時間の変更,棚卸など)の情報を格納する。これは、データ入力手段(101)で入力されたものであり、1000回のシミュレーションの場合ならば
1000組のデータが存在する。
In the past demand, weather information, calendar, and event DB (302), as shown in FIG. 4, each customer's past demand for each unit time (1 hour or 30 minutes), the location of the customer Stores weather information (temperature, weather, etc.), calendar (day of the week, holidays, etc.), and event information for each customer (regular holiday, sale, opening / closing time change, inventory, etc.). This is input by the data input means (101), and 1000 sets of data exist in the case of 1000 simulations.
予測値を算出(303)では、DB(301),(302)を用いて、各予測モデルによる各需要家の単位時間ごとの予測値を算出する。例えば、ある需要家の3月1日の13時の需要量を予測する場合、図4の(401)のように、DB(301)に格納されている全予測モデルを用いて、3月1日13時の予測値を算出する。また、後述する予測モデルの精度評価(305)において、評価方法として例えば過去1か月における予測誤差の標準偏差を用いる場合は、過去1ヶ月分の予測値も算出しておく。   In the calculation of the predicted value (303), the predicted values for each unit time of each consumer based on the respective prediction models are calculated using the DBs (301) and (302). For example, when predicting the demand amount of a certain customer at 13:00 on March 1, using all prediction models stored in the DB (301) as shown in (401) of FIG. Calculate the predicted value for 13:00 a day. In addition, in the accuracy evaluation (305) of the prediction model, which will be described later, when the standard deviation of the prediction error in the past month is used as the evaluation method, for example, the predicted value for the past month is also calculated.
予測値DB(304)には、(303)で算出した(501)のような結果を格納する。   The prediction value DB (304) stores the result (501) calculated in (303).
予測モデルの精度評価(305)では、各モデルの精度を定量的に評価する。評価方法の一例としては、各予測モデルを用いて過去1ヶ月間の日々の予測をしたときの予測誤差の標準偏差、もしくは予測誤差の絶対値の平均値などが挙げられる。また、寄与率やAIC(Akaike's Information Criteria)などを用いて評価する方法もある。寄与率については先述の「多変量解析法入門(永田靖・棟近雅彦共著)」P.16 を、AICについては朝倉書店の「非線形時系列解析(松葉育雄著)」P.44〜P.47などを参照されたい。尚、例として示した(502)では、過去1ヶ月の予測誤差の標準偏差によって評価している。   In the accuracy evaluation (305) of the prediction model, the accuracy of each model is quantitatively evaluated. As an example of the evaluation method, there is a standard deviation of a prediction error or a mean value of an absolute value of the prediction error when daily prediction is performed for the past one month using each prediction model. There is also a method of evaluation using a contribution rate or AIC (Akaike's Information Criteria). For the contribution rate, see “Introduction to the Multivariate Analysis Method” (by Nagisa Nagata and Masahiko Munechika) P.16, and for AIC, “Nonlinear Time Series Analysis (Ikuo Matsuba)” by Asakura Shoten, P.44-P.47. Please refer to. In (502) shown as an example, the evaluation is based on the standard deviation of prediction errors for the past month.
最良の予測モデルの選択(306)では、(305)の結果から、最も予測精度が高いと判断されるものも選択する。(502)では、最も標準偏差の小さいモデル2が選択される。   In selecting the best prediction model (306), the one that is determined to have the highest prediction accuracy from the result of (305) is also selected. In (502), the model 2 with the smallest standard deviation is selected.
予測値確定(307)では、(306)で選択した予測モデルによる予測値を、予測値DB(304)から検索し、その値を最終的な予測値として確定する。図4の例では、
(402)よりモデル2が選択されたので、(501)の3月1日の予測値の110kWを最終的な予測値とする。
In the prediction value confirmation (307), the prediction value based on the prediction model selected in (306) is searched from the prediction value DB (304), and the value is confirmed as the final prediction value. In the example of FIG.
Since model 2 is selected from (402), 110 kW of the predicted value on March 1 of (501) is set as the final predicted value.
需要予測誤差分布算出手段(103)は、需要量期待値予測手段(102)の予測誤差の確率分布を算出する。例えば、過去1ヶ月などの一定期間における、日々の需要予測値と実績値の差、即ち予測誤差から、誤差の経験分布を導く。もしくは、なんらかの確率分布を仮定してもよい。つまり、例えば正規分布を仮定し、予測誤差の期待値と標準偏差のみを推定してもよい。   The demand prediction error distribution calculation means (103) calculates the probability distribution of the prediction error of the demand amount expected value prediction means (102). For example, an error empirical distribution is derived from the difference between the daily demand forecast value and the actual value in a certain period such as the past month, that is, the forecast error. Alternatively, some probability distribution may be assumed. That is, for example, assuming a normal distribution, only the expected value and standard deviation of the prediction error may be estimated.
発電可能量期待値予測手段(104)については、図6を用いて説明する。気温などによる発電可能量の変動や経年劣化などの発電機特性DB(601)と、気温や天気などの気象情報、定期点検などの発電所のイベントなどの情報DB(602)をもとに、発電および抽気可能量を予測(603)し、予測値を出力(604)する。尚、DB(602)は、データ入力手段(101)で入力したものである。   The possible power generation amount expected value predicting means (104) will be described with reference to FIG. Based on the generator characteristics DB (601) such as fluctuations in the amount of power that can be generated due to temperature and deterioration over time, weather information such as temperature and weather, and information DB (602) such as power plant events such as periodic inspections, The power generation and bleedable amount is predicted (603), and the predicted value is output (604). The DB (602) is input by the data input means (101).
発電可能量予測誤差分布算出手段(105)は、発電可能量期待値予測手段(104)の予測誤差の確率分布を算出する。例えば図5に示すように、過去1ヶ月などの一定期間における、日々の需要予測値と実績値の差、即ち予測誤差(501)から、(502)のような誤差の経験分布を求める。もしくは、予測誤差が正規分布などの確率分布に従うものとして、予測誤差の平均値と標準偏差だけを計算して、(503)のような分布を得てもよい。   The power generation possible amount prediction error distribution calculating means (105) calculates the probability distribution of the prediction error of the power generation possible amount expected value prediction means (104). For example, as shown in FIG. 5, an error empirical distribution such as (502) is obtained from the difference between the daily demand forecast value and the actual value in a certain period such as the past month, that is, the prediction error (501). Alternatively, assuming that the prediction error follows a probability distribution such as a normal distribution, only the average value and standard deviation of the prediction error may be calculated to obtain a distribution such as (503).
外部調達電力量算出手段(106)は、需要量期待値予測手段(102)と需要予測誤差分布算出手段(103)と発電可能量期待値予測手段(104)と発電可能量予測誤差分布算出手段(105)の結果を用いて、電力会社など契約発電所以外から買う外部調達電力量を算出する。その方法について、図7を用いて詳しく説明する。図7は、電力の供給不足量の確率分布である。その期待値は、需要予測値−発電可能量予測値で与えられ、これは需要量期待値予測手段(102)と需要予測誤差分布算出手段(103)と発電可能量期待値予測手段(104)より求める。また確率分布の形は、需要予測誤差分布算出手段(103)と発電可能量予測誤差分布算出手段(105)より求める。これも経験分布でもよいし、正規分布などの確率分布を仮定してもよい。ある一定量b(kW/h)以上の電力が供給不足となる確率は、図7の斜線部の面積で与えられる。外部調達電力は、各単位時間において一定量以上不足する確率、もしくは、ある一定時間以上にわたって一定量以上不足する確率を設定し、これを制約条件として、発電所からの電力と外部調達電力の調達コストの和が最小となるような外部調達電力量を算出する。   The externally procured power amount calculation means (106) includes a demand amount expected value prediction means (102), a demand prediction error distribution calculation means (103), a power generation amount expected value prediction means (104), and a power generation amount prediction error distribution calculation means. Using the result of (105), the amount of electric power procured from outside the contracted power plant such as an electric power company is calculated. The method will be described in detail with reference to FIG. FIG. 7 is a probability distribution of an insufficient supply amount of power. The expected value is given by demand predicted value-power generation possible amount predicted value, which is demand amount expected value predicting means (102), demand prediction error distribution calculating means (103), and power generation possible amount expected value predicting means (104). Ask more. The shape of the probability distribution is obtained from the demand prediction error distribution calculation means (103) and the power generation possible amount prediction error distribution calculation means (105). This may also be an empirical distribution, or a probability distribution such as a normal distribution may be assumed. The probability that the electric power of a certain amount b (kW / h) or more will be insufficiently supplied is given by the area of the hatched portion in FIG. Procurement of power from the power plant and externally procured power is set as a constraint condition, with the probability of a shortage of more than a certain amount in each unit time, or a probability of shortage of a certain amount over a certain amount of time. Calculate the amount of externally procured power that minimizes the sum of costs.
電力調達コスト算出手段(107)は、発電所や電力会社との契約内容をもとに、過去一定期間における発電所からの電力と外部調達電力の総調達コストを算出する。   The power procurement cost calculation means (107) calculates the total procurement cost of the power from the power plant and the externally procured power in the past fixed period based on the contract contents with the power plant and the power company.
事業収益算出手段(108)は、(107)で算出した電力調達コスト,電力託送コスト,人件費などの費用と、電力を売ることで得られる収入から、過去一定期間における収益を計算する。   The business profit calculating means (108) calculates the profit for a certain period in the past from the power procurement cost, the power consignment cost, the labor cost, etc. calculated in (107) and the income obtained by selling the power.
事業収益表示手段(109)は、事業収益算出手段(108)の結果を、CRTやTFTなどのディスプレイ装置に出力する。   The business profit display means (109) outputs the result of the business profit calculation means (108) to a display device such as a CRT or TFT.
このように本発明の実施例は、以下の手段からなる。   Thus, the Example of this invention consists of the following means.
データ入力手段は、過去の需要実績値もしくは需要実績値の分布をもとに発生させた乱数と、過去の発電可能量実績値もしくは発電可能量実績値の分布をもとに発生させた乱数と、需要予測に必要な情報(需要家所在地の気温や天気,需要家の過去の需要実績値やイベントなどの情報,曜日や祝日などのカレンダー情報など)と、発電可能量予測に必要な情報(発電所所在地の気温,発電機の定期検査等のイベントの情報など)を、入力もしくはデータベース(以下DB)から取り出す。   The data input means includes a random number generated based on the past actual demand value or distribution of actual demand value, and a random number generated based on the past actual power generation amount actual value or distribution of actual power generation amount actual value. , Information necessary for demand forecasting (such as temperature and weather at the customer's location, past demand actual values and events, calendar information such as days of the week and holidays), and information necessary for forecasting power generation capacity ( The temperature of the power plant location, information on events such as periodic inspections of the generator, etc.) are input or retrieved from a database (hereinafter referred to as DB).
需要予測手段は、需要量期待値予測手段と需要予測誤差分布算出手段から成る。需要量期待値予測手段は、上記需要予測に必要な情報を用いて、電力や蒸気の需要を予測し、
DBに格納する。需要予測誤差分布算出手段は、需要量期待値予測手段の予測誤差の分布を算出し、DBに格納する。
The demand prediction means includes demand quantity expected value prediction means and demand prediction error distribution calculation means. The demand amount expected value prediction means predicts the demand for electric power and steam using the information necessary for the above demand prediction,
Store in DB. The demand prediction error distribution calculation unit calculates the distribution of the prediction error of the demand amount expected value prediction unit and stores it in the DB.
発電可能量予測手段は、発電可能量期待値予測手段と発電可能量予測誤差分布算出手段から成る。ここで発電可能量とは、発電所がPPSへ送電可能な最大電力量のことをさしている。つまり、発電所が発電した電力の一部を社内で使用し、残りをPPSに送電する場合は、発電機のフル出力から社内使用分だけ引いた値である。   The possible power generation amount predicting means includes a possible power generation amount expected value prediction means and a possible power generation amount prediction error distribution calculation means. Here, the amount of power that can be generated refers to the maximum amount of power that the power plant can transmit to the PPS. That is, when a part of the power generated by the power plant is used in-house and the rest is transmitted to the PPS, the value is obtained by subtracting only the in-house use from the full output of the generator.
発電可能量期待値予測手段は、上記発電可能量予測に必要な情報を用いて、電力や蒸気の売買契約をしている発電所もしくは自社の発電所もしくはその両方の、発電可能量と抽出可能蒸気量を予測し、DBに格納する。発電可能量予測誤差分布算出手段は、発電可能量期待値予測手段の予測誤差の分布を算出し、DBに格納する。   The expected power generation amount prediction means can extract the amount of power generation possible from power plants and / or in-house power plants that have power or steam sales contracts using the information required for the above power generation forecasts. The steam amount is predicted and stored in the DB. The possible power generation amount prediction error distribution calculating means calculates the prediction error distribution of the possible power generation amount expected value prediction means and stores it in the DB.
外部調達電力量算出手段は、需要予測値とその予測誤差分布および発電可能量予測値とその予測誤差分布をDBから取り出し、それらをもとに、外部調達電力量を算出し、DBに格納する。電力調達コスト算出手段は、外部調達電力量をDBから取り出して外部調達電力コストを算出し、それに取引契約をしている発電所もしくは自社の発電所から調達するのにかかるコストを足して、DBに格納する。   The externally procured power amount calculation means takes out the demand prediction value and its prediction error distribution and the power generation possible amount prediction value and its prediction error distribution from the DB, calculates the externally procured power amount based on them, and stores them in the DB. . The power procurement cost calculation means calculates the externally procured power cost by taking out the amount of externally procured power from the DB, and adds the cost required to procure from the power plant that has a business contract or its own power plant to the DB To store.
事業収益算出手段は、電力調達コスト算出手段の結果をDBから取り出し、一定期間における期待収益と分散を計算し、DBに格納する。事業収益表示手段は、事業収益算出手段で算出した結果をDBから取り出し、ディスプレイ装置に表示する。   The business profit calculating means takes out the result of the power procurement cost calculating means from the DB, calculates the expected profit and variance for a certain period, and stores them in the DB. The business profit display means takes out the result calculated by the business profit calculation means from the DB and displays it on the display device.
このように、本発明により、電力調達コストが最小、もしくは収益が最大となるような外部調達電力予約量を、予約する時点で(即ち翌日の需要量が分からない状態で)合理的に予測することができる。また、特定規模電力事業者が、特定の1件以上の需要家と電力取引契約をした場合の電力調達コストをより正確に推定することにより、運用収益をより正確に推定することができる。   As described above, according to the present invention, the externally procured power reservation amount that minimizes the power procurement cost or maximizes the profit is reasonably predicted at the time of reservation (ie, the demand amount on the next day is not known). be able to. In addition, it is possible to more accurately estimate the operating revenue by more accurately estimating the power procurement cost when a specific scale electric power company makes a power transaction contract with one or more specific customers.
なお、本発明の実施例では、入力手段で受付けたデータをハードウェアであるDBに記憶する手段を有し、DBに記憶されたデータに基づいて各手段で演算が行われる。そしてまた、各手段の演算結果をDBに記憶する記憶手段を有し、記憶されたDBに基づいて再度各手段で演算が行われたり、表示装置に表示されたりしている。すなわち、ハードウェアとソフトウェアが協働して本発明は達成される。   In the embodiment of the present invention, there is provided means for storing data received by the input means in a DB, which is hardware, and the calculation is performed by each means based on the data stored in the DB. In addition, it has a storage means for storing the calculation results of each means in the DB, and the calculation is again performed by each means based on the stored DB or displayed on the display device. That is, the present invention is achieved by cooperation of hardware and software.
本発明の一実施例の機能ブロック図。The functional block diagram of one Example of this invention. 本発明の一実施例のデータ入力手段の説明図。Explanatory drawing of the data input means of one Example of this invention. 本発明の一実施例の需要量期待値予測手段の処理フロー図。The processing flow figure of the demand amount expected value prediction means of one Example of this invention. 本発明の一実施例の需要量期待値予測手段の説明図。Explanatory drawing of the demand amount expected value prediction means of one Example of this invention. 本発明の一実施例の外部調達電力量算出手段の説明図。Explanatory drawing of the external procurement electric energy calculation means of one Example of this invention. 本発明の一実施例の発電可能量予測誤差分布算出手段の説明図。Explanatory drawing of the electric power generation possible amount prediction error distribution calculation means of one Example of this invention. 電力の供給不足量の確率分布の一例。An example of a probability distribution of an insufficient amount of power supply.
符号の説明Explanation of symbols
101…データ入力手段、102…需要量期待値予測手段、103…需要予測誤差分布算出手段、104…発電可能量期待値予測手段、105…発電可能量予測誤差分布算出手段、106…外部調達電力量算出手段、107…電力調達コスト算出手段、108…事業収益算出手段、109…事業収益表示手段、110…需要予測手段、111…発電可能量予測手段、201…需要量期待値予測手段で用いるDBの説明図、701…供給不足量の確率密度関数。   DESCRIPTION OF SYMBOLS 101 ... Data input means 102 ... Demand amount expected value prediction means 103 ... Demand prediction error distribution calculation means 104 ... Power generation expected amount prediction value prediction means 105 ... Power generation possible amount prediction error distribution calculation means 106 ... Externally procured electric power Quantitative calculation means 107, power procurement cost calculation means 108, business profit calculation means 109, business profit display means 110, demand prediction means 111, power generation possible quantity prediction means 201, demand quantity expected value prediction means DB explanatory diagram, 701... Probability density function of supply shortage.

Claims (10)

  1. 自社設備もしくは契約先設備で発電するとともに、外部電力を購入することにより電力を調達して、需要家に供給する電力事業の電力供給計画に適用される電力供給事業リスク管理支援システムであって、
    カレンダー,需要家のイベント,天気,過去の需要量、又は気温のデータと、発電機の特性に関する情報とを受付けるデータ入力手段と、
    前記データ入力手段で受付けたデータをデータベースに記憶する記憶手段と、
    前記データベースに記憶された前記カレンダー,需要家のイベント,天気,過去の需要量、又は気温のデータにより、電力や蒸気の需要を予測する需要予測手段と、
    前記需要予測手段で予測された需要予測結果をデータベースに記憶する記憶手段と、
    前記データベースに記憶された前記発電機の特性に関する情報と、気温のデータにより、自社設備もしくは契約先設備による発電可能量を予測する発電可能量予測手段と、
    前記発電可能量予測手段で予測された発電可能量予測結果をデータベースに記憶する記憶手段と、
    前記データベースに記憶された前記需要予測結果と前記発電可能量予測結果により、調達すべき外部調達電力量を算出する外部調達電力量算出手段と、
    を備えることを特徴とする電力供給事業リスク管理支援システム。
    A power supply business risk management support system applied to a power supply plan of a power business that generates power by own equipment or contracted equipment, procures power by purchasing external power, and supplies it to consumers,
    Data input means for receiving calendar, consumer events, weather, past demand or temperature data, and information on generator characteristics;
    Storage means for storing data received by the data input means in a database;
    Demand forecasting means for forecasting demand for electric power and steam from the calendar, consumer events, weather, past demand, or temperature data stored in the database;
    Storage means for storing the demand prediction result predicted by the demand prediction means in a database;
    Information relating to the characteristics of the generator stored in the database, and data on the temperature, power generation potential prediction means for predicting the power generation possible amount by the company equipment or contracted equipment,
    Storage means for storing a power generation possible amount prediction result predicted by the power generation possible amount prediction means in a database;
    An externally procured power amount calculating means for calculating an externally procured power amount to be procured based on the demand prediction result and the power generation potential prediction result stored in the database;
    Power supply business risk management support system characterized by comprising:
  2. 請求項1において、
    前記外部調達電力量算出手段で算出した外部調達電力量算出結果をデータベースに記憶する記憶手段とを有し、
    データベースに記憶された前記需要予測結果と前記発電可能量予測結果と前記外部調達電力量算出結果をもとに、電力供給計画の事業収益の期待値とリスクを算出する事業収益算出手段とを備えることを特徴とする電力供給事業リスク管理支援システム。
    In claim 1,
    Storage means for storing the externally procured power amount calculation result calculated by the externally procured power amount calculating means in a database;
    Business revenue calculation means for calculating an expected value and risk of business revenue of a power supply plan based on the demand forecast result, the power generation potential forecast result and the externally procured power amount computation result stored in a database This is a power supply business risk management support system.
  3. 請求項1において、
    前記需要予測手段として、需要量の期待値を予測する需要量期待値予測手段と需要予測誤差を算出する需要予測誤差分布算出手段を備えることを特徴とする電力供給事業リスク管理支援システム。
    In claim 1,
    A power supply business risk management support system comprising: a demand amount expectation value prediction means for predicting an expected value of a demand amount; and a demand prediction error distribution calculation means for calculating a demand prediction error as the demand prediction means.
  4. 請求項3において、
    需要予測誤差分布算出手段が算出する需要予測誤差が確率分布として与えられ、
    予測履歴データより得られた経験的確率分布、あるいは予測履歴データを用いて所与の確率分布関数のパラメータを決めた確率分布として算出することを特徴とする電力供給事業リスク管理支援システム。
    In claim 3,
    The demand forecast error calculated by the demand forecast error distribution calculation means is given as a probability distribution,
    A power supply business risk management support system characterized by calculating an empirical probability distribution obtained from prediction history data or a probability distribution in which parameters of a given probability distribution function are determined using the prediction history data.
  5. 請求項3において、
    前記需要予測誤差分布算出手段による需要予測誤差の確率分布から算出した計画対象時区間の需要の確率分布の下で、外部調達電力量を最適化することを特徴とする電力供給事業リスク管理システム。
    In claim 3,
    A power supply business risk management system for optimizing the amount of externally procured power under the probability distribution of demand in the planned time interval calculated from the probability distribution of demand prediction error by the demand prediction error distribution calculating means.
  6. 請求項1において、
    前記発電可能量予測手段として、発電可能量の期待値を算出する発電可能量期待値予測手段と発電可能量の予測誤差の分布を算出する発電可能量予測誤差算出手段とを備えることを特徴とする電力供給事業リスク管理支援システム。
    In claim 1,
    As the possible power generation amount predicting means, comprising: an expected power generation amount predicting value calculating means for calculating an expected value of the possible power generation amount; and a possible power generation amount prediction error calculating means for calculating a prediction error distribution of the possible power generation amount, Power supply business risk management support system.
  7. 請求項6において、
    発電可能量予測誤差算出手段が算出する発電可能量予測誤差が確率分布として与えられ、予測履歴データより得られた経験的確率分布、あるいは予測履歴データを用いて所与の確率分布関数のパラメータを決めた確率分布として算出することを特徴とする電力供給事業リスク管理支援システム。
    In claim 6,
    The generation potential prediction error calculated by the power generation amount prediction error calculation means is given as a probability distribution, and the empirical probability distribution obtained from the prediction history data or the parameters of a given probability distribution function using the prediction history data A power supply business risk management support system characterized by being calculated as a determined probability distribution.
  8. 請求項6において、
    前記発電可能量予測誤差算出手段による発電可能量予測誤差の確率分布から算出した計画対象時区間の発電可能量の確率分布の下で、外部調達電力量を最適化することを特徴とする電力供給事業リスク管理システム。
    In claim 6,
    A power supply characterized by optimizing an externally procured power amount under a probability distribution of a possible power generation amount in a planned time interval calculated from a probability distribution of a possible power generation amount prediction error by the possible power generation amount prediction error calculating means Business risk management system.
  9. 請求項1乃至請求項8の何れかにおいて、
    前記外部調達電力量算出手段において事業収益の期待値が最大となる外部調達電力量を算出することを特徴とする電力供給事業リスク管理支援システム。
    In any one of Claims 1 to 8,
    A power supply business risk management support system, characterized in that the externally procured power amount calculation means calculates an externally procured power amount that maximizes the expected value of business revenue.
  10. 請求項1乃至請求項8の何れかにおいて、
    前記外部調達電力量算出手段において外部調達電力コストの期待値が最小となる外部調達電力量を算出することを特徴とする電力供給事業リスク管理支援システム。

    In any one of Claims 1 to 8,
    The power supply business risk management support system, wherein the externally procured power amount calculating means calculates an externally procured power amount that minimizes an expected value of the externally procured power cost.

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